Beyond the technological excitement, beyond the narratives of industrial revolutions and inevitable transformations, the way companies are using AI today points to a little-understood reality: artificial intelligence is, in most cases, an efficiency innovation.
What do we often see? We see companies implementing AI to reduce costs, speed up processes, eliminate errors, automate repetitive tasks, and increase productivity.
This has profound consequences for how resources are redistributed, roles are redefined, and competitive advantages are repositioned.
To understand the real impact of this way of using AI in business, it is useful to turn to Clayton Christensen’s theory of innovation, as it clarifies not only what is happening, but also what is coming next.
1. What Clayton Christensen’s theory says about innovation
Professor Clayton Christensen has redefined the way we understand innovation, showing that not all innovations are created equal and, more importantly, that not all of them produce growth.
Essentially, he identified three types:
• Efficiency innovation: Reduces costs, optimizes processes, increases productivity. It does not necessarily create new markets, but it frees up resources.
• Sustaining innovation: Improves existing products for existing customers. It increases value, but does not change the market structure.
• Disruptive innovation: Creates new markets, changes the competitive game, and shifts power to new players.
Clayton Christensen emphasized that most companies invest heavily in efficiency innovations because they are the easiest to justify financially and the fastest to implement. Disruptive innovations, on the other hand, are rare, risky, and difficult to manage.
2. Why AI is, in practice, an efficiency innovation
Today, we see that AI is not yet creating completely new markets on a global scale. Instead, it makes companies faster, more efficient, and more scalable. Here are the characteristics that make AI an efficiency innovation:
a. AI optimizes processes, not business models
About 70-80% of current AI adoption falls into the efficiency innovation category. Implementations are geared towards reducing costs, eliminating errors, and accelerating internal flows.
Back-office automation, automated report generation, customer support via chatbots, rapid data analysis, or document processing are typical examples.
AI thus becomes an optimization engine, not a strategic transformation engine. It improves existing processes, increasing speed and accuracy, without fundamentally changing the way the company creates value.
b. AI increases productivity, without changing the market
Artificial intelligence allows companies to produce more, faster, and with fewer resources, but it does not change the market structure or customer behavior. Companies keep the same products and customers, but the production method becomes more efficient.
AI speeds up internal activities, reduces execution times, and improves operational consistency. The productivity increase is visible, but it does not generate new demand or a new category of consumers. It is an internal improvement, not an external change in the competitive game.
c. AI frees up resources, not creates new demands
According to Professor Christensen’s theory, AI is a technology that “creates capacity,” not “creates demand.” Thus, AI frees up time, capital, and human energy, allowing companies to do more with less.
This is the central mechanism of efficiency innovation: you optimize processes, reduce costs, and obtain additional resources that you can reinvest.
AI does not automatically bring in new customers or new markets; it only improves the way the company operates. The impact is internal, operational, and paves the way for eventual sustainable or disruptive innovations.
d. AI is often adopted for a fast ROI
Disruptive innovations have a slow ROI, difficult to predict, and often uncertain. In contrast, AI promises immediate and measurable results, which is why companies are adopting it at an accelerated pace.
Automating repetitive tasks, reducing operational costs, and increasing execution speed produce visible benefits within weeks or months.
AI thus becomes a pragmatic investment, easy to justify to the board and aligned with efficiency objectives. A technology delivers quickly, without requiring radical changes in strategy or business model.
3. The negative impact of AI on the business environment
Although AI brings rapid operational gains, its predominant use as an efficiency innovation produces a series of adverse effects on the business environment, especially when it is adopted massively and without systemic balancing measures.
a. Increase in youth unemployment
The automation of repetitive tasks directly affects entry-level roles, those that represent the gateway to the job market for young people. Back-office, customer support, document processing, primary data analysis, are all areas where AI is quickly taking over activities with low cognitive value. The result ends up being fewer career entry opportunities and tougher competition for the remaining roles.
b. Margin erosion through customer pressure
As AI becomes standard, customers start to see efficiency as a right rather than an advantage. If all players can produce faster and cheaper, the pressure shifts to price. Margins are squeezed, and companies are forced to deliver more with less, without being able to monetize technological differentiation. Efficiency thus becomes a race in which only the lowest cost wins.
c. Competitive polarization between large and small companies
We are already seeing how AI favors large players, who have access to data, infrastructure and capital. SMEs, on the other hand, either cannot afford the initial integration costs, or do not have relevant data volumes and cannot scale as quickly. The result is a polarized market in which corporations become increasingly efficient, and small companies lose ground, being pushed into ever narrower niches.
d. Product standardization and loss of differentiation
When AI optimizes processes, companies tend to adopt the same solutions, the same models, the same flows. Products become increasingly similar, experiences become increasingly standardized, and real differentiation erodes. Instead of creating uniqueness, AI can generate a market of operational copy-paste, in which supporting innovation is reduced to minor adjustments.
e. Over-reliance on technology providers
Companies can become dependent on technology ecosystems that are difficult to replace. This dependence reduces strategic autonomy, increases the risk of lock-in, and limits the ability to change direction quickly. Instead of being a tool for flexibility, AI can become a factor of rigidity.
f. Increased vulnerability to systemic errors
As processes are automated, errors are no longer just individual, but systemic. A wrong decision generated by a model simultaneously affects thousands of transactions, customers, or processes. Companies become more efficient, but also more fragile: a bug, a breach, or a poorly trained model can cause major problems in a very short time.
In conclusion
Artificial intelligence, used predominantly as an efficiency innovation, not only optimizes operations, but also reconfigures risks across the entire business ecosystem. The negative effects do not come from the technology, but from the way it is adopted: quickly, uniformly, cost-oriented and without systemic measures to balance efficiency and real innovation.
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